Python Environments (Local or Kubeflow)#
Connecting to the Feature Store from any Python environment requires setting up a Feature Store API key and installing the library. This guide explains step by step how to connect to the Feature Store from any Python environment such as your local environment or KubeFlow.
Generate an API key#
In Hopsworks, click on your username in the top-right corner and select Settings to open the user settings. Select API keys. Give the key a name and select the job, featurestore and project scopes before creating the key. Copy the key into your clipboard.
Create a file called
featurestore.key in your designated Python environment and save the API key from your clipboard in the file.
The API key should contain at least the following scopes:
You are only able to retrieve the API key once. If you did not manage to copy it to your clipboard, delete it and create a new one.
To be able to access the Hopsworks Feature Store, the
HSFS Python library needs to be installed in the environment from which you want to connect to the Feature Store. You can install the library through pip. We recommend using a Python environment manager such as virtualenv or conda.
pip install hsfs[python]~=[HOPSWORKS_VERSION]
HSFS assumes Spark/EMR is used as execution engine and therefore Hive dependencies are not installed. Hence, on a local Python evnironment, if you are planning to use a regular Python Kernel without Spark/EMR, make sure to install the "python" extra dependencies (
Matching Hopsworks version
The major version of
HSFS needs to match the major version of Hopsworks.
Connect to the Feature Store#
You are now ready to connect to the Hopsworks Feature Store from your Python environment:
import hsfs conn = hsfs.connection( host='my_instance', # DNS of your Feature Store instance port=443, # Port to reach your Hopsworks instance, defaults to 443 project='my_project', # Name of your Hopsworks Feature Store project api_key_value='apikey', # The API key to authenticate with Hopsworks hostname_verification=True # Disable for self-signed certificates ) fs = conn.get_feature_store() # Get the project's default feature store
HSFS uses either Apache Spark or Pandas on Python as an execution engine to perform queries against the feature store. The
engine option of the connection let's you overwrite the default behaviour by setting it to
"spark". By default,
HSFS will try to use Spark as engine if PySpark is available. So if you have PySpark installed in your local Python environment, but you have not configured Spark, you will have to set
engine='python'. Please refer to the Spark integration guide to configure your local Spark cluster to be able to connect to the Hopsworks Feature Store.
If you have trouble to connect, please ensure that your Feature Store can receive incoming traffic from your Python environment on ports 443, 9083 and 9085 (443,9083,9085).
If you deployed your Hopsworks Feature Store instance with managed.hopsworks.ai, it suffices to enable outside access of the Feature Store and Online Feature Store services.
For more information about how to connect, see the Connection guide. Or continue with the Data Source guide to import your own data to the Feature Store.